(1) Field of Invention
The present invention relates to a system for automatic object localization and, more particularly, to a system for automatic object localization using simultaneous localization and mapping (SLAM) and cognitive swarm recognition.
(2) Description of Related Art
Object localization is a technique that can be used to identify the location of a particular object. Detection of objects of interest in an unknown environment is often performed manually. For example, in military applications, soldiers must visually spot the locations of the objects of interest (e.g., potential threats).
Some products and prototype systems exist for detecting and defending against potential threats, but only after the threatened act has occurred (e.g., a weapon firing). Such existing systems include “Boomerang II” by BBN Technologies, which is located at 10 Moulon Street, Cambridge, Mass. 02138; “Crosshairs” by the Defense Advanced Research Projects Agency (DARPA), which is located at 3701 North Fairfax Drive, Arlington, Va. 22203; “WeaponWatch” by Radiance Technologies, located at 350 Wynn Drive, Huntsville, Ala. 35805; “Robot Enhanced Detection Outpost with Lasers (REDOWL)” by iRobot Corporation, located at 8 Crosby Drive, Bedford, Mass. 01730; “PDCue Tetrahedral Gunfire Detection System” by AAI Corporation, located at 124 Industry Lane, Hunt Valley, Md. 21030; “Anti-Sniper Infrared Targeting System (ASITS)” by M2 Technologies, Inc., which is located at 945 Concord Street Suite 217/218, Framingham, Mass. 01701; and “ShotSpotter” by ShotSpotter, Inc., located at 1060 Terra Bella Avenue, Mountain View, Calif. 94043.
Each of the systems described above use post-threat localization techniques. Additionally, the systems described do not provide pose detection (e.g., aiming, partially occluded, kneeling) of a potential threat, which is essential in accurate threat detection. Most existing systems depend on acoustic sensing for fire detection, while a few use image-based approaches with infrared (IR) sensors.
Particle swarm optimization (PSO) is a technique that can be applied to object recognition. PSO was first described by Kennedy, J., Eberhart, R. C., and Shi, Y. in “Swarm Intelligence,” San Francisco: Morgan Kaufmann Publishers, 2001. PSO was also described by R. C. Eberhart and Y. Shi in “Particle Swarm Optimization: Developments, Applications, and Resources,” 2001, which is incorporated by reference as though fully set forth herein. Cognitive swarms are a new variation and extension of PSO. Cognitive swarms search for and recognize objects by combining PSO with an objective function that is based on the recognition confidence.
Simultaneous localization and mapping (SLAM) is a technique used to generate a map within an unknown environment (or a known environment). While generating the map, SLAM enables predicting and updating of the current location of the moving device (e.g., robot, autonomous vehicle) by discerning the device's relative movement from a set of sensors. While the SLAM technique has been actively used for autonomous navigation, augmented reality, and vision-guided robotics, the technique has not been applied to object localization.
Thus, a continuing need exists for a system which combines efficient object recognition with an environmental mapping capability to provide rapid and accurate object localization.